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Please use this identifier to cite or link to this item: http://hdl.handle.net/1860/1578

Title: Automated question answering over the web: An adaptive search and retrieval strategy
Authors: Israel, Quinsulon L.
Keywords: Pattern recognition;Question-answering systems;Query expansion;Search strategy;Strategy selection;Text mining;Web mining;Natural language processing;Search engine
Issue Date: 17-Apr-2007
Publisher: Drexel University. College of Information Science and Technology.
Series/Report no.: IST Research Day 2007 posters
Abstract: The problem of efficiently finding answers to natural language questions over the web has gained much attention. Currently, useful experimental models for implementing question answering work well only for smaller, specific collections of documents and/or they only handle short, single factoid-type questions. Other more generally focused models retrieve and re-rank only a set of documents most likely to contain an answer. These approaches rely on only a few specific strategies to implement question answering. A more comprehensive and dynamic model of a question answering system may provide better performance for both retrieving candidate answer pools and extracting specific answers. Such a new model will be designed that efficiently combines automatic question reformulation, search strategy selection, query expansion, and answer extraction/pooling techniques. The system will automatically learn question reformulation for the most popular web search engines, based on training collections of question answer pairs, such as FAQs. Questions will be matched against automatically learned question types and reformulated into queries based on answer phrases likely to appear in a document containing the answer. The semantic answer type will also be determined based on the question type and used to recognize potential answers. During system training, the top ranked documents retrieved by the search engine will be examined for their likelihood of containing an appropriate answer. User answer-acceptance feedback will be collected to re-rank documents entries and/or refine new queries as necessary during live runs.
URI: http://hdl.handle.net/1860/1578
Appears in Collections:Research Day Posters (IST)

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